Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Computing and Informatics Research
Amelia Belinda Silviana
Universitas Gunadarma, Depok
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sistem Pendukung Keputusan Penerima BANPRES UMKM Penanganan Covid-19 Menerapkan Metode WASPAS Mochamad Dedy Subekti Rahardjo; Amelia Belinda Silviana; Dini Andriyani; Agus Turiyono
Journal of Computing and Informatics Research Vol 5 No 3 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i3.3119

Abstract

The Covid-19 pandemic has had a significant impact on the sustainability of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. To assist affected MSMEs, the government launched the Presidential Assistance for Micro Business Productiveness (BANPRES UMKM) program. However, in its implementation, the process of determining aid recipients often faces obstacles such as inaccurate targeting, lack of objectivity, and time constraints in the selection process. Therefore, a system is needed that can assist in effective and efficient decision-making. This study aims to design a Decision Support System (DSS) to determine BANPRES UMKM recipients by applying the Weighted Aggregated Sum Product Assessment (WASPAS) method. The WASPAS method was chosen because it is able to integrate the advantages of the Weighted Sum Model (WSM) and Weighted Product Model (WPM) methods, resulting in more stable and accurate calculations in multi-criteria problems. The criteria used in this system include business legality, length of business operation, level of losses due to the pandemic, number of family dependents, and asset ownership. System testing results demonstrate that the WASPAS method is capable of objectively ranking alternatives, thus supporting a more transparent and targeted selection process. This research is expected to provide a technological solution to support data-driven social assistance distribution and measurable decision-making logic.
Co-Authors Agus Turiyono Dini Andriyani Mochamad Dedy Subekti Rahardjo